Are You Listening Correctly to What Your Data Says While Performing Some Analysis?

This was a question asked in one of the conferences pertaining to Big Data and Analytics.

Well, most of us would not know whether we really extract the right data and use it correctly until we get some consequences, good or bad out of them.

Although several enterprises conduct an in-depth analysis of their Big Data, there are several times they fail to achieve the desired objective out of it due to a faulty data structure, complex algorithms, complicated logic, unstructured data layers and heterogeneous volume and variety of data.

In these scenarios, the only way to ensure your data is correctly processed and analyzed is through Big Data testing. Big data testing helps you get rid of complexity in data by verifying quality, integrity, and health of your data. When performed rigorously, it can help you validate the accuracy of the data to enhance processes and actions based on that.

Besides the above aids, Big Data testing can be beneficial in several ways. Here are the top 10 ways how Big Data testing can be beneficial to your enterprise:

1. Reduces Downtime

There are several applications that run on the data. In the case of bad data, it hampers the effectiveness and performance of the application. Many times while collecting and deploying data on applications, enterprises fail to identify the data health which leads to downtime. Big data testing in these cases can help improve data quality and related processes of the application which further reduces the overall downtime.

2. Ensures Reliability and Effectiveness

The process of collecting and fetching data from different data sources and channels involve chances of obtaining ineffective, inaccurate, and unreliable data. Faulty and ineffective data doubles the risk of failure in case of applications that run on real-time data. Big data testing checks the data from its root till the end, which includes verification of data layers, components, and logic. This helps ensure reliability and effectiveness of data.

3. Lessens Threat to Data Quality

Every enterprise wants their data to be valid, consistent, precise and unique. If it lacks any of these, there are chances of a threat to the quality of data. However, undergoing rigorous testing of data can save data from becoming degradable and redundant. Thus, big data testing can lessen the data quality threat.

4. Scale Data Sets

To begin with any application development cycle, enterprises start with small data sets and gradually shift to the larger ones. Applications based on smaller data sets work great. But, what if the results get changed with the different data sets? There are chances of application failure when the data sets change or increase. Enterprises can avoid these problems by adding testing process as an integral part of their application lifecycle to ensure the performance do not get affected by small or big changes in data sets.

5. Provides Data Security and Authenticity

It is highly important for enterprises which deal with client applications and host their data on their server to secure the confidentiality of data to maintain trust imposed by their clients. If data security is breached, it can have serious implications for the brand. It is advisable to perform big data testing at different levels and intervals to avoid chances of data security breach and vulnerability. It can also help audit the data which further assists in maintaining data authentication and integrity.

6. Optimize Processes

Big Data and Predictive Analytics are the backbones of several processes occurring at enterprises in various fields. Performing big data testing can be of great help to ensure all the data used by these processes are clean, accurate, and healthy which helps in avoiding loopholes in these processes.

7. Validates Real-time Data

For big data application to interact with live data, some sort of filtration and analysis is required to ensure the data captured is valid and productive. In these scenarios, performance testing of data ensures that the application processes correct and qualified data in real-time.

8. Ensures Consistency

Enterprises use a variety of applications that deal to and fro with different data sets which results in rising data inconsistencies. In cases where the results obtained from Big Data and Predictive Analytics are not consistent, it can be a big fat disgrace for enterprises. Testing of data helps determine the variability in data beforehand and perform necessary actions to avoid uncertainty in the results.

9. Supports Data Digitization

Though it is a period of digitization, every enterprise has some sort data or documents in paper format. If the enterprise plans to convert them into the digital format, it is necessary to maintain the confidentiality and content and see to it that none of the data gets corrupted post digitization. With adequate testing, enterprises can avoid the threat of data getting lost, redundant or corrupted.

10. Improves ROI

Enterprises need to be competitive in the Big Data and Predictive Analytics strategy. Adding testing as a mandatory activity before any analysis and processing will ensure that the enterprises listen to the correct and accurate data which helps obtain the best output. This further aids in improving the ROI and win over competitors.

All the above points show that the Big Data testing is important for enterprises to take corrective decisions and process right information for performing necessary functions. All they need to have is the right testing strategies in place to make the best out of their Big Data testing investments.

So what are your thoughts on making big data testing an integral part of your big data implementation strategy? Do you wish to add any other benefits of big data testing from your experiences? If yes, mention in the comments section below.